{"id":"W4390874202","doi":"10.1109/iccv51070.2023.01639","title":"LDL: Line Distance Functions for Panoramic Localization","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation","keywords":"Computer science; Computer vision; Artificial intelligence; Line (geometry); Panorama; Computation; Feature (linguistics); Matching (statistics); Line segment; Pipeline (software); Point (geometry); Complement (music); Distance transform; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004319712,0.00006840277,0.00006577985,0.00006573904,0.00006587085,0.00002602164,0.00003421045,0.00004361703,0.00003480134],"category_scores_gemma":[0.0000216589,0.00006702817,0.00003008615,0.0003781715,0.000008988803,0.00005065917,0.000004383818,0.00002641345,0.0001186873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002762584,"about_ca_system_score_gemma":0.000005642483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004185716,"about_ca_topic_score_gemma":0.00003939631,"domain_scores_codex":[0.9995801,0.000003988855,0.0001251641,0.00009194414,0.00006252759,0.0001362592],"domain_scores_gemma":[0.9997711,0.00003392351,0.000008965849,0.0001068756,0.00004799633,0.00003119286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002219964,0.000004917211,0.0001429445,0.00003791359,0.000007166006,3.092673e-7,0.00001769258,0.9770578,0.0004387586,0.005177481,0.01564612,0.001466722],"study_design_scores_gemma":[0.0001451621,0.00001590898,0.000125837,0.000006853456,0.000007622325,2.164332e-7,0.0000354266,0.9586537,0.0006958713,0.0005128513,0.03971415,0.00008636487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002236331,0.0000251625,0.9949589,0.000116045,0.0004096571,0.0001538846,0.00002015571,0.0009033225,0.001176515],"genre_scores_gemma":[0.9941545,0.0000567174,0.0009776825,0.00007821266,0.0001162609,0.0000307617,0.0005993178,0.00004013519,0.003946421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9939812,"threshold_uncertainty_score":0.273333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654758300205196,"score_gpt":0.225970340759013,"score_spread":0.209422757756961,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}